




import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_lowerCamelCase      :						int							=  collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_lowerCamelCase      :						List[str]							=  """https://storage.googleapis.com/cvdf-datasets/mnist/"""







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			int:



       """simple docstring"""

       A__ 					=       numpy.dtype(numpy.uintaa		).newbyteorder('''>'''		)
       return numpy.frombuffer(bytestream.read(4		)					,							dtype=lowercase_		)[0]







@deprecated(lowercase_					,							'''Please use tf.data to implement this functionality.'''		)
def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[Any]:



       """simple docstring"""

       print('''Extracting'''					,							f.name		)
       with gzip.GzipFile(fileobj=lowercase_		) as bytestream:
              A__ 					=       _readaa(lowercase_		)
              if magic != 2_051:
                     raise ValueError(
                         '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)		)
              A__ 					=       _readaa(lowercase_		)
              A__ 					=       _readaa(lowercase_		)
              A__ 					=       _readaa(lowercase_		)
              A__ 					=       bytestream.read(rows * cols * num_images		)
              A__ 					=       numpy.frombuffer(lowercase_					,							dtype=numpy.uinta		)
              A__ 					=       data.reshape(lowercase_					,							lowercase_					,							lowercase_					,							1		)
              return data







@deprecated(lowercase_					,							'''Please use tf.one_hot on tensors.'''		)
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       A__ 					=       labels_dense.shape[0]
       A__ 					=       numpy.arange(lowercase_		) * num_classes
       A__ 					=       numpy.zeros((num_labels, num_classes)		)
       A__ 					=       1
       return labels_one_hot







@deprecated(lowercase_					,							'''Please use tf.data to implement this functionality.'''		)
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False					,							lowercase_=10		)  ->			List[Any]:



       """simple docstring"""

       print('''Extracting'''					,							f.name		)
       with gzip.GzipFile(fileobj=lowercase_		) as bytestream:
              A__ 					=       _readaa(lowercase_		)
              if magic != 2_049:
                     raise ValueError(
                         '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)		)
              A__ 					=       _readaa(lowercase_		)
              A__ 					=       bytestream.read(lowercase_		)
              A__ 					=       numpy.frombuffer(lowercase_					,							dtype=numpy.uinta		)
              if one_hot:
                     return _dense_to_one_hot(lowercase_					,							lowercase_		)
              return labels


class 							UpperCamelCase_       :

    '''simple docstring'''






    @deprecated(
        UpperCAmelCase__   ,					'''Please use alternatives such as official/mnist/_DataSet.py'''
        ''' from tensorflow/models.'''   ,					)
    def __init__(     self :      Tuple   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      int=False   ,					UpperCAmelCase__ :      int=False   ,					UpperCAmelCase__ :      str=dtypes.floataa   ,					UpperCAmelCase__ :      Dict=True   ,					UpperCAmelCase__ :      Dict=None   ,					)		->List[Any]:





           '''simple docstring'''

           A__					,				A__ 					=       random_seed.get_seed(UpperCAmelCase__)
           # If op level seed is not set, use whatever graph level seed is returned
           numpy.random.seed(seeda if seed is None else seeda)
           A__ 					=       dtypes.as_dtype(UpperCAmelCase__).base_dtype
           if dtype not in (dtypes.uinta, dtypes.floataa):
                  raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype)
           if fake_data:
                  A__ 					=       10_000
                  A__ 					=       one_hot
           else:
                  assert (
                      images.shape[0] == labels.shape[0]
                  ), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
                  A__ 					=       images.shape[0]

                  # Convert shape from [num examples, rows, columns, depth]
                  # to [num examples, rows*columns] (assuming depth == 1)
                  if reshape:
                         assert images.shape[3] == 1
                         A__ 					=       images.reshape(
                             images.shape[0]   ,					images.shape[1] * images.shape[2])
                  if dtype == dtypes.floataa:
                         # Convert from [0, 255] -> [0.0, 1.0].
                         A__ 					=       images.astype(numpy.floataa)
                         A__ 					=       numpy.multiply(UpperCAmelCase__   ,					1.0 / 255.0)
           A__ 					=       images
           A__ 					=       labels
           A__ 					=       0
           A__ 					=       0


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Dict:





           '''simple docstring'''

           return self._images


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Optional[int]:





           '''simple docstring'''

           return self._labels


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->int:





           '''simple docstring'''

           return self._num_examples


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Tuple:





           '''simple docstring'''

           return self._epochs_completed





    def 			SCREAMING_SNAKE_CASE				(     self :      str   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[Any]=False   ,					UpperCAmelCase__ :      int=True)		->Optional[int]:





           '''simple docstring'''

           if fake_data:
                  A__ 					=       [1] * 784
                  A__ 					=       [1] + [0] * 9 if self.one_hot else 0
                  return (
                      [fake_image for _ in range(UpperCAmelCase__)],
                      [fake_label for _ in range(UpperCAmelCase__)],
                  )
           A__ 					=       self._index_in_epoch
           # Shuffle for the first epoch
           if self._epochs_completed == 0 and start == 0 and shuffle:
                  A__ 					=       numpy.arange(self._num_examples)
                  numpy.random.shuffle(UpperCAmelCase__)
                  A__ 					=       self.images[perma]
                  A__ 					=       self.labels[perma]
           # Go to the next epoch
           if start + batch_size > self._num_examples:
                  # Finished epoch
                  self._epochs_completed += 1
                  # Get the rest examples in this epoch
                  A__ 					=       self._num_examples - start
                  A__ 					=       self._images[start : self._num_examples]
                  A__ 					=       self._labels[start : self._num_examples]
                  # Shuffle the data
                  if shuffle:
                         A__ 					=       numpy.arange(self._num_examples)
                         numpy.random.shuffle(UpperCAmelCase__)
                         A__ 					=       self.images[perm]
                         A__ 					=       self.labels[perm]
                  # Start next epoch
                  A__ 					=       0
                  A__ 					=       batch_size - rest_num_examples
                  A__ 					=       self._index_in_epoch
                  A__ 					=       self._images[start:end]
                  A__ 					=       self._labels[start:end]
                  return (
                      numpy.concatenate((images_rest_part, images_new_part)   ,					axis=0),
                      numpy.concatenate((labels_rest_part, labels_new_part)   ,					axis=0),
                  )
           else:
                  self._index_in_epoch += batch_size
                  A__ 					=       self._index_in_epoch
                  return self._images[start:end], self._labels[start:end]







@deprecated(lowercase_					,							'''Please write your own downloading logic.'''		)
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Optional[int]:



       """simple docstring"""

       if not gfile.Exists(lowercase_		):
              gfile.MakeDirs(lowercase_		)
       A__ 					=       os.path.join(lowercase_					,							lowercase_		)
       if not gfile.Exists(lowercase_		):
              urllib.request.urlretrieve(lowercase_					,							lowercase_		)  # noqa: S310
              with gfile.GFile(lowercase_		) as f:
                     A__ 					=       f.size()
              print('''Successfully downloaded'''					,							lowercase_					,							lowercase_					,							'''bytes.'''		)
       return filepath







@deprecated(
    lowercase_					,							'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''		)
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False					,							lowercase_=False					,							lowercase_=dtypes.floataa					,							lowercase_=True					,							lowercase_=5_000					,							lowercase_=None					,							lowercase_=DEFAULT_SOURCE_URL					,							)  ->			Any:



       """simple docstring"""

       if fake_data:

              def fake():
                     return _DataSet(
                         []					,							[]					,							fake_data=lowercase_					,							one_hot=lowercase_					,							dtype=lowercase_					,							seed=lowercase_		)

              A__ 					=       fake()
              A__ 					=       fake()
              A__ 					=       fake()
              return _Datasets(train=lowercase_					,							validation=lowercase_					,							test=lowercase_		)

       if not source_url:  # empty string check
              A__ 					=       DEFAULT_SOURCE_URL

       A__ 					=       '''train-images-idx3-ubyte.gz'''
       A__ 					=       '''train-labels-idx1-ubyte.gz'''
       A__ 					=       '''t10k-images-idx3-ubyte.gz'''
       A__ 					=       '''t10k-labels-idx1-ubyte.gz'''

       A__ 					=       _maybe_download(
           lowercase_					,							lowercase_					,							source_url + train_images_file		)
       with gfile.Open(lowercase_					,							'''rb'''		) as f:
              A__ 					=       _extract_images(lowercase_		)

       A__ 					=       _maybe_download(
           lowercase_					,							lowercase_					,							source_url + train_labels_file		)
       with gfile.Open(lowercase_					,							'''rb'''		) as f:
              A__ 					=       _extract_labels(lowercase_					,							one_hot=lowercase_		)

       A__ 					=       _maybe_download(
           lowercase_					,							lowercase_					,							source_url + test_images_file		)
       with gfile.Open(lowercase_					,							'''rb'''		) as f:
              A__ 					=       _extract_images(lowercase_		)

       A__ 					=       _maybe_download(
           lowercase_					,							lowercase_					,							source_url + test_labels_file		)
       with gfile.Open(lowercase_					,							'''rb'''		) as f:
              A__ 					=       _extract_labels(lowercase_					,							one_hot=lowercase_		)

       if not 0 <= validation_size <= len(lowercase_		):
              A__ 					=       (
                  '''Validation size should be between 0 and '''
                  f"""{len(lowercase_		)}. Received: {validation_size}."""
              )
              raise ValueError(lowercase_		)

       A__ 					=       train_images[:validation_size]
       A__ 					=       train_labels[:validation_size]
       A__ 					=       train_images[validation_size:]
       A__ 					=       train_labels[validation_size:]

       A__ 					=       {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}

       A__ 					=       _DataSet(lowercase_					,							lowercase_					,							**lowercase_		)
       A__ 					=       _DataSet(lowercase_					,							lowercase_					,							**lowercase_		)
       A__ 					=       _DataSet(lowercase_					,							lowercase_					,							**lowercase_		)

       return _Datasets(train=lowercase_					,							validation=lowercase_					,							test=lowercase_		)









'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



